Sammenlign metoder

	Stokastisk Tabu Search	Partikelsværmoptimering (PSO)
Fagområde	Simulering	Optimering
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1990s	1995
Ophavsperson	Glover, F. (base TS); stochastic extensions by various authors (1990s–2000s)	—
Type	Stochastic metaheuristic optimizer	Population-based metaheuristic / swarm intelligence
Oprindelig kilde	Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4), 74-94.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Aliasser	STS, Randomized Tabu Search, Probabilistic Tabu Search, Noisy Tabu Search	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Relaterede	5	6
Resumé	Stochastic Tabu Search (STS) is an extension of classical Tabu Search that introduces randomness into the neighborhood exploration and move-selection phases. By combining tabu memory — which forbids recently visited solutions — with probabilistic acceptance or random candidate sampling, STS escapes local optima more effectively and explores rugged solution landscapes that deterministic TS may fail to traverse.	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.